Aeberhard, Jasmin Leonie; Radan, Anda-Petronela; Soltani, Ramin Abolfazl; Strahm, Karin Maya; Schneider, Sophie; Carrié, Adriana; Lemay, Mathieu; Krauss, Jens; Delgado-Gonzalo, Ricard; Surbek, Daniel (2024). Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding-An Interdisciplinary Project. Methods and protocols, 7(1) MDPI AG 10.3390/mps7010005
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Artificial intelligence (AI) is gaining increasing interest in the field of medicine because of its capacity to process big data and pattern recognition. Cardiotocography (CTG) is widely used for the assessment of foetal well-being and uterine contractions during pregnancy and labour. It is characterised by inter- and intraobserver variability in interpretation, which depends on the observers' experience. Artificial intelligence (AI)-assisted interpretation could improve its quality and, thus, intrapartal care. Cardiotocography (CTG) raw signals from labouring women were extracted from the database at the University Hospital of Bern between 2006 and 2019. Later, they were matched with the corresponding foetal outcomes, namely arterial umbilical cord pH and 5-min APGAR score. Excluded were deliveries where data were incomplete, as well as multiple births. Clinical data were grouped regarding foetal pH and APGAR score at 5 min after delivery. Physiological foetal pH was defined as 7.15 and above, and a 5-min APGAR score was considered physiologic when reaching ≥7. With these groups, the algorithm was trained to predict foetal hypoxia. Raw data from 19,399 CTG recordings could be exported. This was accomplished by manually searching the patient's identification numbers (PIDs) and extracting the corresponding raw data from each episode. For some patients, only one episode per pregnancy could be found, whereas for others, up to ten episodes were available. Initially, 3400 corresponding clinical outcomes were found for the 19,399 CTGs (17.52%). Due to the small size, this dataset was rejected, and a new search strategy was elaborated. After further matching and curation, 6141 (31.65%) paired data samples could be extracted (cardiotocography raw data and corresponding maternal and foetal outcomes). Of these, half will be used to train artificial intelligence (AI) algorithms, whereas the other half will be used for analysis of efficacy. Complete data could only be found for one-third of the available population. Yet, to our knowledge, this is the most exhaustive and second-largest cardiotocography database worldwide, which can be used for computer analysis and programming. A further enrichment of the database is planned.
Item Type: |
Journal Article (Original Article) |
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Division/Institute: |
04 Faculty of Medicine > Department of Gynaecology, Paediatrics and Endocrinology (DFKE) > Clinic of Gynaecology |
UniBE Contributor: |
Radan, Anda-Petronela, Strahm, Karin Maya, Schneider, Sophie, Carrié, Adriana, Surbek, Daniel |
Subjects: |
600 Technology > 610 Medicine & health |
ISSN: |
2409-9279 |
Publisher: |
MDPI AG |
Language: |
English |
Submitter: |
Pubmed Import |
Date Deposited: |
22 Jan 2024 17:19 |
Last Modified: |
22 Jan 2024 17:29 |
Publisher DOI: |
10.3390/mps7010005 |
PubMed ID: |
38251198 |
Uncontrolled Keywords: |
artificial intelligence (AI) cardiotocography (CTG) deep learning (DL) foetal monitoring machine learning (ML) neural network (NN) obstetrics |
BORIS DOI: |
10.48350/191978 |
URI: |
https://boris.unibe.ch/id/eprint/191978 |
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